Lung Squamous Cell Carcinoma: Correlation between miRseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 548 genes and 11 clinical features across 282 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • HSA-MIR-125A

  • 5 genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • HSA-LET-7G ,  HSA-MIR-499 ,  HSA-MIR-491 ,  HSA-MIR-26B ,  HSA-MIR-3653

  • 4 genes correlated to 'HISTOLOGICAL.TYPE'.

    • HSA-MIR-29A ,  HSA-MIR-181A-2 ,  HSA-MIR-490 ,  HSA-MIR-100

  • 9 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • HSA-MIR-26A-1 ,  HSA-MIR-628 ,  HSA-MIR-326 ,  HSA-MIR-361 ,  HSA-MIR-16-1 ,  ...

  • 1 gene correlated to 'NEOADJUVANT.THERAPY'.

    • HSA-MIR-3145

  • No genes correlated to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=1 older N=1 younger N=0
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=5 higher score N=0 lower score N=5
HISTOLOGICAL TYPE ANOVA test N=4        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=9        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NEOADJUVANT THERAPY t test N=1 yes N=1 no N=0
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-173.8 (median=15.1)
  censored N = 161
  death N = 105
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

One gene related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 67.83 (8.6)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-125A 0.2362 7.643e-05 0.0419

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-125A to 'AGE'. P value = 7.64e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 75
  MALE 207
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

5 genes related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 24.78 (38)
  Significant markers N = 5
  pos. correlated 0
  neg. correlated 5
List of 5 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S6.  Get Full Table List of 5 genes significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-LET-7G -0.6209 4.152e-06 0.00228
HSA-MIR-499 -0.738 1.681e-05 0.0092
HSA-MIR-491 -0.5683 3.784e-05 0.0207
HSA-MIR-26B -0.5603 5.129e-05 0.028
HSA-MIR-3653 -0.55 7.522e-05 0.0409

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-LET-7G to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 4.15e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

4 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 5
  LUNG PAPILLARY SQUAMOUS CELL CARCINOMA 1
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 1
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 274
     
  Significant markers N = 4
List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of 4 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
HSA-MIR-29A 7.162e-08 3.9e-05
HSA-MIR-181A-2 5.985e-06 0.00325
HSA-MIR-490 3.4e-05 0.0184
HSA-MIR-100 8.724e-05 0.0472

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-29A to 'HISTOLOGICAL.TYPE'. P value = 7.16e-08 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.99 (0.75)
  N
  T1 64
  T2 173
  T3 29
  T4 16
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.5 (0.72)
  N
  N0 173
  N1 79
  N2 25
  N3 4
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

9 genes related to 'PATHOLOGICSPREAD(M)'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 247
  M1 3
  MX 26
     
  Significant markers N = 9
List of 9 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of 9 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSA-MIR-26A-1 9.696e-08 5.31e-05
HSA-MIR-628 7.083e-07 0.000387
HSA-MIR-326 2.903e-06 0.00159
HSA-MIR-361 2.61e-05 0.0142
HSA-MIR-16-1 2.91e-05 0.0158
HSA-MIR-1277 4.1e-05 0.0223
HSA-MIR-320E 7.128e-05 0.0386
HSA-MIR-374C 7.633e-05 0.0413
HSA-MIR-1180 9.087e-05 0.0491

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-26A-1 to 'PATHOLOGICSPREAD(M)'. P value = 9.7e-08 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.71 (0.83)
  N
  Stage 1 144
  Stage 2 75
  Stage 3 57
  Stage 4 3
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 10
  YES 272
     
  Significant markers N = 0
Clinical variable #11: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S15.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 28
  YES 254
     
  Significant markers N = 1
  Higher in YES 1
  Higher in NO 0
List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

Table S16.  Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
HSA-MIR-3145 5.22 4.243e-05 0.0232 0.7921

Figure S5.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3145 to 'NEOADJUVANT.THERAPY'. P value = 4.24e-05 with T-test analysis.

Methods & Data
Input

  • Expresson data file = LUSC.miRseq_RPKM_log2.txt

  • Clinical data file = LUSC.clin.merged.picked.txt

  • Number of patients = 282

  • Number of genes = 548

  • Number of clinical features = 11

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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